scholarly journals Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts*

Santiago Olivella ◽  
Tyler Pratt ◽  
Kosuke Imai
2021 ◽  
pp. 1471082X2110331
Giacomo De Nicola ◽  
Benjamin Sischka ◽  
Göran Kauermann

Mixture models are probabilistic models aimed at uncovering and representing latent subgroups within a population. In the realm of network data analysis, the latent subgroups of nodes are typically identified by their connectivity behaviour, with nodes behaving similarly belonging to the same community. In this context, mixture modelling is pursued through stochastic blockmodelling. We consider stochastic blockmodels and some of their variants and extensions from a mixture modelling perspective. We also explore some of the main classes of estimation methods available and propose an alternative approach based on the reformulation of the blockmodel as a graphon. In addition to the discussion of inferential properties and estimating procedures, we focus on the application of the models to several real-world network datasets, showcasing the advantages and pitfalls of different approaches.

1978 ◽  
Vol 9 (4) ◽  
pp. 220-235
David L. Ratusnik ◽  
Carol Melnick Ratusnik ◽  
Karen Sattinger

Short-form versions of the Screening Test of Spanish Grammar (Toronto, 1973) and the Northwestern Syntax Screening Test (Lee, 1971) were devised for use with bilingual Latino children while preserving the original normative data. Application of a multiple regression technique to data collected on 60 lower social status Latino children (four years and six months to seven years and one month) from Spanish Harlem and Yonkers, New York, yielded a small but powerful set of predictor items from the Spanish and English tests. Clinicians may make rapid and accurate predictions of STSG or NSST total screening scores from administration of substantially shortened versions of the instruments. Case studies of Latino children from Chicago and Miami serve to cross-validate the procedure outside the New York metropolitan area.

2015 ◽  
Vol 21 ◽  
pp. 301
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 7-15 ◽  
Joachim Gerich ◽  
Roland Lehner

Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.

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